Abstract
ObjectivesThis study aimed to systematically review recent health economic evaluations (HEEs) of artificial intelligence (AI) applications in healthcare. The aim was to discuss pertinent methods, reporting quality and challenges for future implementation of AI in healthcare, and additionally advise future HEEs. MethodsA systematic literature review was conducted in 2 databases (PubMed and Scopus) for articles published in the last 5 years. Two reviewers performed independent screening, full-text inclusion, data extraction, and appraisal. The Consolidated Health Economic Evaluation Reporting Standards and Philips checklist were used for the quality assessment of included studies. ResultsA total of 884 unique studies were identified; 20 were included for full-text review, covering a wide range of medical specialties and care pathway phases. The most commonly evaluated type of AI was automated medical image analysis models (n = 9, 45%). The prevailing health economic analysis was cost minimization (n = 8, 40%) with the costs saved per case as preferred outcome measure. A total of 9 studies (45%) reported model-based HEEs, 4 of which applied a time horizon >1 year. The evidence supporting the chosen analytical methods, assessment of uncertainty, and model structures was underreported. The reporting quality of the articles was moderate as on average studies reported on 66% of Consolidated Health Economic Evaluation Reporting Standards items. ConclusionsHEEs of AI in healthcare are limited and often focus on costs rather than health impact. Surprisingly, model-based long-term evaluations are just as uncommon as model-based short-term evaluations. Consequently, insight into the actual benefits offered by AI is lagging behind current technological developments.
Highlights
Within the healthcare sector, artificial intelligence (AI) has seen a substantial rise in development over the past years because of growing interest and its potential impact on healthcare delivery and effectiveness.[1]
This systematic review exposes an important gap in the methods used for health economic evaluations (HEEs) of AI applications in healthcare
In the context of health economics, the cheetah of AI innovation is only at a slow pace pursued by the tortoise
Summary
Artificial intelligence (AI) has seen a substantial rise in development over the past years because of growing interest and its potential impact on healthcare delivery and effectiveness.[1]. AI is growing in different domains of healthcare, from the automation of clinical workflows to the interpretation of clinical findings and the prediction of health outcomes, treatment response, and disease recurrence.[3] At the rate at which AI applications are being developed, augmented, and used, AI creates an opportunity for accessible and evidence-based decision making within the global health community.[4] In the areas of image processing and electronic health record interpretation, medical decision support through text mining, and the analysis of medical time series data (ie, longitudinal data on blood pressure, electrocardiograms), the use of AI has shown promising results.[5,6,7] processing digital health data with AI could support the delivery of effective and efficient healthcare.[8] even though the advancement of AI carries much potential, what value AI can and will deliver in actual clinical practice remains a central question and proper implementation guidance is crucial.
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